Full Stack AI Engineer
Skills
About This Role
Overview
We are looking for two highly capable Full-Stack AI Developers with proven experience building AI-native products and intelligent automation systems from the ground up.
The ideal candidates should be comfortable working across the full stack and owning end-to-end delivery of new user-facing features, from rapid 0-to-1 prototyping to production-ready implementation.
This includes building agentic AI workflows, real-time collaborative experiences, and scalable AI-powered applications.
Candidates must have hands-on experience working with LLMs, workflow automation, prompt orchestration, API design/integration, and modern AI development frameworks.
Strong engineering fundamentals across frontend, backend, and cloud infrastructure are essential.
You should be confident working with relational and NoSQL databases, designing scalable architectures, and deploying applications across cloud environments such as AWS, Azure, or GCP.
Experience
across web, backend, and mobile application development is required.
Technoloy Expertise Required
- Python for backend development, automation, AI integrations, and workflow orchestration
- React, TypeScript, and Next.js for building scalable, responsive, and high-quality web applications
- Node.js, Express for backend services, API development, and real-time application logic
- Cloud infrastructure across AWS, Azure, or GCP, including deployment, scalability, monitoring, and production operations
- Experience integrating these technologies into AI-native products, real-time systems, and end-to-end digital platforms
Preferred Experience
- 08 Years of Experience in Full Stack Engineering including Web & Mobile Development
- Building and delivering web and mobile applications across the full product lifecycle
- Integrating LLMs into real-world applications, including prompt design, AI tooling, and orchestration
- Designing agentic AI workflows, automation pipelines, and AI-enabled business processes
- Developing real-time systems, collaborative features, and event-driven applications
- Building and consuming REST APIs, WebSockets, and microservices-based architectures
- Working with LLMs, vector databases, RAG pipelines, embeddings, and AI search/retrieval systems
- Implementing CI/CD pipelines, scalable deployments, cloud-native infrastructure, and production-grade engineering practices
- Taking ownership of features end to end - from architecture and development to deployment, monitoring, and iteration
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